3 ms·
> are always better in every scenario than loose ones. Sounds a little unconvincing. > applying some kind of averaging filter How do you choose the size of y
by Hackbraten 5y ago
> are always better in every scenario than loose ones.
Sounds a little unconvincing.
> applying some kind of averaging filter
How do you choose the size of your averaging window?
How do you make sure you don’t catch a systematic bias due to some unrelated event?
Control theory may help in some cases (flight control) but possibly less so in others (stock market).
- CraigJPerry 5y ago>> Sounds a little unconvincing. Yeah, it is pretty wooly. How about taking it from the opposite position, almost like a proof by contradiction, e.g. the opposite position might be “you can see better if you look less often”.
- Retric 5y agoDepends on the assumptions, tight feedback loops can be based on less information each loop. For example an image sensor collecting light. In that example you can have excessively tight feedback loops where looking less often let’s you see better.
- fastball 5y agoI feel like you're conflating "sampling rate" with "tight feedback loop". Yes, a higher sampling rate (assuming each sample remains at or above the level of quality of your lower rate samples) is always better. But a tighter feedback loop is one where you are making decisions with that data at a faster clip too. As pointed out in the article (and by yourself), this is not always good. If you are using some averaging filter, as you say, then that is being "looser" about your feedback loop. You are recognizing that just because the accelerometer is telling you that you are going down doesn't mean you are about to crash. Someone with an overly tight feedback loop would not be so careful, and might immediately course-correct as soon as they sense they are going down unintentionally.
- ledauphin 5y agoI would read the opposing position as "you can see better if you look for longer periods of time".